Gartner Announces Top 10 Strategic Technology Trends for 2019
Fusion of trends and trends, leading to new innovations
Technologies Beyond the Introduction Stage, Peaking Within 5 Years
Gartner has announced the major strategic technology trends that enterprises should pay attention to in 2019.

“Intelligence, Digital and Mesh have been consistent themes for the past two years and will continue to be major growth drivers through 2019,” said David Cearley, vice president and Gartner Fellow. “Trends under these three themes will be key elements in driving the continuous innovation process as part of a ‘ContinuousNEXT’ strategy.”

ContinuousNEXT is about continuously building momentum beyond digital innovation, and the five essential elements for implementing this are ▲Privacy ▲Augmented Intelligence ▲Culture ▲Product Management ▲Digital Twin.

Additionally, Gartner Vice President David Salisbury said, "AI and augmented intelligence, for example, in the form of automated things, areence) is used together with IoT, edge computing, and digital twins to provide highly integrated smart spaces. The 10 strategic technology trends presented by Gartner for 2019 are characterized by a comprehensive impact that creates new opportunities and induces new innovations as multiple trends converge," he emphasized.

Gartner's top 10 strategic technology trends for 2019 are as follows:
1. Autonomous Things
Autonomous things, such as robots, drones, and self-driving cars equipped with AI, automatically handle tasks that humans do. The level of automation of autonomous things goes beyond the level of automation through programming models. The AI of autonomous things exhibits advanced behaviors that naturally interact with the surrounding environment.

For example, a drone might survey a large field, conclude that it is ready for harvest, and then activate an “autonomous harvester.” Additionally, robots and drones could perform delivery work using self-driving cars.

“As autonomous things proliferate, we will move beyond standalone intelligent things to a mix of intelligent things that follow human instruction or can collaborate with other devices on their own,” said VP David Salisbury.


2. Augmented Analytics
Augmented analytics, which uses AI to analyze big data, will rapidly evolve into a key function of data preparation, data management, modern analytics, business process management, process mining, and data science platforms.

Insights gained from automated augmented analytics will be applied to business operations across HR, finance, sales, marketing, customer service, procurement, and asset management departments, optimizing decisions and actions for all employees, including analysts and data scientists.

Augmented analytics automates the process of preparing data, generating insights, and visualizing those insights, thereby reducing the need for specialized data scientists. Data analysis becomes possible even outside of the IT department.
“Augmented analytics will lead to citizen data science, as it enables users who are not data analytics savvy to derive insights from data,” said VP David Salisbury. “By 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists.”

He also said, “In the future, organizations will be able to leverage citizen data scientists to address the data science and machine learning talent shortage caused by the shortage and high cost of data scientists.”


3. AI-Driven Development
In the past, collaboration between data scientists and application developers was essential to create AI-enhanced solutions. Recently, this method has been rapidly changing to a model where professional developers can operate independently using predefined models as a service. The predefined models as a service provide professional developers with an ecosystem of AI algorithms and models, as well as development tools designed to integrate models and AI capabilities into their solutions.

Another level of professional application development opportunity arises as AI is applied to the development process itself to automate various data science, application development, and testing functions. By 2022, it is expected that at least 40% of new application development projects will have AI co-developers on their teams.
“Ultimately, advanced AI-driven development environments that automate both functional and non-functional aspects of applications will usher in a new era of ‘citizen application developers,’ where non-professionals will be able to use AI-driven tools to automatically create new solutions,” said VP David Salisbury. “Tools that enable non-professionals to create applications without coding are not new, but we expect that AI-driven systems will enable a new level of flexibility.”


4. Digital Twins
A digital twin is a digital version of an object or system that exists in the real world. Gartner estimates that by 2020, there will be more than 20 billion connected sensors and endpoints, and that digital twins will exist for potentially billions of things. Organizations will initially implement digital twins in simple ways. Over time, they will evolve to improve their ability to collect and visualize the right data, apply the right analytics and rules, and effectively respond to business objectives.

“One aspect of the digital twin evolution that goes beyond IoT is enterprises implementing Digital Twins of an Organizations (DTOs),” said VP David Salisbury. “DTOs are dynamic software models that rely on operational or other data to understand how an organization operates its business model, connects to its current state, deploys resources, and responds to changes to deliver customer value.”

He added, “DTOs not only increase the efficiency of business processes, but also help create flexible, dynamic and responsive processes that can automatically react to changing situations.”


5. Empowered Edge
Edge refers to endpoint devices that people use or are embedded in our surroundings. Edge computing is a computing topology where information processing, content collection, and delivery are handled close to the endpoints. Edge computing attempts to handle traffic and processing locally to reduce traffic and latency.

In the near future, the edge will be driven by IoT, and processing will be done close to the endpoint rather than on a centralized cloud server. And cloud computing and edge computing will evolve into complementary models where cloud services are managed as central services, not just on central servers, but also on distributed on-premises and edge devices themselves.

Over the next five years, advanced AI chips with greater processing power, storage, and other advanced features will be embedded in a variety of edge devices. In the long term, as 5G communications mature, the expanded edge computing environment will establish more robust communications with central services. 5G communications provide lower latency per km3, higher bandwidth, and, most importantly, an increased number of nodes (edge endpoints) at the edge.


6. Immersive Experience
Conversational platforms are changing the way people interact with the digital world. Virtual reality (VR), augmented reality (AR), and mixed reality (MR) are changing the way people perceive the digital world. This integrated shift in perception and interaction models leads to future immersive user experiences.
“Over time, we will shift from thinking about individual devices and fragmented user interface (UI) technologies to a multichannel and multimodal experience,” said VP David Salisbury. “The multimodal experience will connect people with the digital world and hundreds of edge devices that surround them, including traditional computing and wearables, automobiles, environmental sensors and appliances.”

Furthermore, he said, "Multichannel experiences will leverage all human senses as well as computer senses (heat, humidity, radar, etc.) across multimodal devices," adding, "This multi-experience environment will allow computers to define our surroundings, rather than individual devices."


7. Blockchain
Blockchain, a type of distributed ledger, promises to reshape industries by building trust, providing transparency, reducing friction between business ecosystems and potentially reducing costs, reducing transaction settlement times, and improving cash flow. Today, credit is built on banks, clearinghouses, governments, and other institutions that act as central authorities. These centralized credit models add delays and friction costs (intermediation fees, commissions, and the time value of money) to financial transactions. Blockchain, on the other hand, provides an alternative mode of credit and eliminates the need for a central authority in arbitrating transactions.

“Current blockchain technologies and concepts are immature, poorly understood and unproven for mission-critical, large-scale business operations – especially when it comes to the complex elements that support more sophisticated scenarios,” said David Salisbury, vice president. “Despite these challenges, the tremendous transformational potential of blockchain technologies means CIOs and IT leaders should begin evaluating the technologies, even if they don’t aggressively adopt them in the next few years.”
Many blockchain initiatives today do not implement all the characteristics of blockchain, including a distributed database. These blockchain-based solutions are used as a means to achieve operational efficiency by automating business processes or digitizing records. They have the potential to enhance information sharing between known entities and improve opportunities for tracking physical and digital assets.

These approaches, however, miss the true value of blockchain innovation and can increase supplier dependency. Organizations that choose this option should understand the limitations and be prepared to transition to a full blockchain solution, while recognizing that the same results can be achieved with more efficient and appropriate use of existing non-blockchain technologies.


8. Smart Spaces
Smart spaces are physical or digital environments where people and technology systems interact in increasingly open, connected, coordinated, and intelligent ecosystems. People, processes, services, and things come together in a smart space to create more immersive, interactive, and automated experiences aimed at target users and industry scenarios.

“This trend has been coalescing around elements such as smart cities, digital workplaces, smart homes and connected factories for some time,” said VP David Salisbury. “As technology becomes an integral part of our daily lives as employees, consumers, customers, members of society or citizens, the market is entering a phase of accelerated delivery of robust smart spaces.”


9. Digital Ethics and Privacy
Digital ethics and privacy are concerns for individuals, organizations, and governments. People are increasingly concerned about how their personal information is used by organizations in both the public and private sectors, and there is growing backlash against organizations that are not taking proactive steps to address these concerns.
“Any discussion on privacy must be grounded in the broader topic of digital ethics and the trust of your customers, constituents and employees. While privacy and security are foundational components of building trust, trust is actually about more than just these components,” said David Salisbury, vice president. “Trust is the acceptance of the truth of a statement without evidence or investigation. Ultimately, an organization’s position on privacy must be driven by a broader concept of ethics and trust. Shifting from privacy to ethics shifts the conversation from ‘Are we compliant?’ to ‘Are we doing the right thing?’”


10. Quantum Computing
Quantum computing is a type of nonclassical computing that operates on the quantum state of subatomic particles, such as electrons and ions, representing information as quantum bits (qbits).

The extraordinary parallel processing capabilities and exponential scalability of quantum computers make them excellent for solving problems that are too complex for traditional approaches or that take too long for traditional algorithms to find solutions.

Industries such as automotive, finance, insurance, pharmaceuticals, military, and research institutions stand to benefit most from advances in quantum computing. In the pharmaceutical industry, for example, quantum computing could be used to model molecular interactions at the subatomic level to speed up the time to market for new cancer drugs. Or quantum computing could be used to more accurately predict protein interactions, leading to new pharmaceutical methodologies.

“CIOs and IT leaders need to plan for quantum computing adoption, learn while the technology is still emerging, identify real-world problems where quantum computing has potential, and consider the potential security implications,” said David Salisbury, vice president.

However, he added, "Most organizations should be learning about and monitoring quantum computing by 2022, because it won't be viable until 2023 or 2025."


As such, the technologies that fall under the 10 strategic trends selected by Gartner for 2019 have already passed the introduction stage and are exerting influence in various fields. These technologies are currently accumulating numerous cases in a wide range of fields, but they still have innovative potential and are expected to reach their peak within the next five years. Companies must respond to this trend, even for their future management strategies.